About the job
Important Information
Location: Remote
Experience: 5-10 years
Job Mode: Full-time
Work Mode: Work from home
About the Role:
We are seeking an experienced MLOps Engineer to join our team and play a key role in the design, development, and deployment of machine learning models and pipelines. The ideal candidate will have a strong background in software engineering, data science, and DevOps practices, with a passion for building scalable and efficient ML systems.
Primarily designing & developing and maintain the infrastructure and tools required for the deployment, monitoring, and continuous improvement of machine learning models and cloud data platform like Databricks
DevOps and DataOps (preferably with Databricks) are mandatory.
Key Responsibilities:
Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. Experience:
Encora is the preferred digital engineering and modernization partner of some of the world's leading enterprises and digital native companies. With over 9,000 experts in 47+ offices and innovation labs worldwide, Encora's technology practices include Product Engineering & Development, Cloud Services, Quality Engineering, DevSecOps, Data & Analytics, Digital Experience, Cybersecurity, and AI & LLM Engineering.
At Encora, we hire professionals based solely on their skills and qualifications, and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.
Location: Remote
Experience: 5-10 years
Job Mode: Full-time
Work Mode: Work from home
About the Role:
We are seeking an experienced MLOps Engineer to join our team and play a key role in the design, development, and deployment of machine learning models and pipelines. The ideal candidate will have a strong background in software engineering, data science, and DevOps practices, with a passion for building scalable and efficient ML systems.
Primarily designing & developing and maintain the infrastructure and tools required for the deployment, monitoring, and continuous improvement of machine learning models and cloud data platform like Databricks
DevOps and DataOps (preferably with Databricks) are mandatory.
Key Responsibilities:
- Model Deployment & Management: Collaborate with data scientists to deploy, scale, and manage machine learning models in production environments.
- Pipeline Automation: Design and implement automated ML pipelines using tools like Kubeflow, MLflow, Airflow, or similar to streamline the ML lifecycle. Design, build, and maintain scalable, reliable data pipelines using ETL/ELT frameworks and tools.
- Data Infrastructure Management: Manage and optimize data platforms, databases, and data lakes (e.g., Hadoop, Spark, Redshift, Smnowflake) to ensure high availability and performance.
- CI/CD for ML and Data pipelines: Develop and maintain CI/CD pipelines for continuous integration, testing, and deployment of ML models and data pipelines.
- Monitoring & Optimization: Implement robust monitoring and logging to ensure model performance, detect anomalies, and manage model drift.
- Data Monitoring & Alerting: Implement monitoring solutions to track data quality, pipeline performance, and infrastructure health, and set up automated alerting and incident management.
- Infrastructure Management: Build and manage scalable infrastructure for ML experiments, including data storage, compute resources, and model serving.
- Collaboration & Best Practices: Work closely with data scientists, data engineers, and DevOps teams to establish and enforce best practices for MLOps and ensure compliance with organizational policies.
- Model Versioning & Experiment Tracking: Use tools like DVC, MLflow, or TensorBoard to track model versions, experiments, results and data.
- Security & Compliance: Ensure that ML solutions and Data Solutions comply with data security, privacy regulations, and best practices.
- Optimization & Troubleshooting: Identify bottlenecks in data pipelines and infrastructure, and work on optimizing them for performance and cost-efficiency
- 9+ years of experience in software engineering, data engineering, or MLOps roles.
- Hands-on experience with cloud platforms (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes).
- Strong experience with services like S3, Lambda, BigQuery, etc.
- Hands-on experience with ETL/ELT tools and frameworks like Apache NiFi, Airflow, dbt, or similar.
- Experience with data storage solutions and data modelling.
- Experience with Medallion architecture scenarios
- Experience with ML tools and frameworks such as TensorFlow, PyTorch, Scikit-Learn, etc.
- Proficiency in programming languages such as Python, with experience in developing and maintaining codebases.
- Familiarity with infrastructure as code (IaC) tools like Terraform or CloudFormation.
- Soft Skills:
- Excellent problem-solving and analytical skills.
- Strong communication and interpersonal skills.
- Ability to work effectively in a fast-paced, dynamic environment.
Encora is the preferred digital engineering and modernization partner of some of the world's leading enterprises and digital native companies. With over 9,000 experts in 47+ offices and innovation labs worldwide, Encora's technology practices include Product Engineering & Development, Cloud Services, Quality Engineering, DevSecOps, Data & Analytics, Digital Experience, Cybersecurity, and AI & LLM Engineering.
At Encora, we hire professionals based solely on their skills and qualifications, and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.
